1 Rmd Settings

2 Contents

covid_on_unemp_benefit_numberのOLSとWLS

3 Read functions/関数の読み込み

source("functions.R")

4 Read data/分析用データの読み込み

df_analysis <- readr::read_csv("output/df_analysis.csv")
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   prefec_kanji = col_character(),
##   prefecture = col_character(),
##   date = col_date(format = ""),
##   prefec = col_character(),
##   prefec_kanji2 = col_character()
## )
## See spec(...) for full column specifications.

5 Main figures in the paper

  • We firstly provide estimations and figures used in the main text.
  • These chunks are copied and pasted from subsequent outcome-based result sections.
  • Actual graphs and tables in the paper are generated and saved in the subsequent chunks, not the chunks in this section. But they are identical.

6 Y = emergency small amount funds/緊急小口の決定件数

6.3 WLS, no trends, post-covid-month dummies, TableC.5 (2)

# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$koguchi_number, 
                    treat_var = df_analysis$unemploy_shock_diff2)

texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)
## 2 coefficients  not defined because the design matrix is rank deficient
## 
## ===================================
##                         Model 1    
## -----------------------------------
## treat_var:date_2020_02             
##                                    
## treat_var:date_2020_03             
##                                    
## treat_var:date_2020_04    18.693   
##                          (21.755)  
## treat_var:date_2020_05    73.809   
##                          (42.459)  
## treat_var:date_2020_06    88.737 **
##                          (32.461)  
## treat_var:date_2020_07    34.658   
##                          (21.043)  
## treat_var:date_2020_08    27.131   
##                          (14.347)  
## treat_var:date_2020_09    21.844   
##                          (15.209)  
## -----------------------------------
## R^2                        0.813   
## Adj. R^2                   0.798   
## Num. obs.                893       
## RMSE                    1373.807   
## N Clusters                47       
## ===================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "unemploy_shock_diff2",
                                 estimation_label = "koguchi_number_WLS")

# Event study graph
graph_koguchi_number_WLS_onlypost <- event_study_graph_2nd_safetynet(data = df_estimates ,
                                          graph_title = "koguchi_number_WLS")

ggplotly(graph_koguchi_number_WLS_onlypost)
estimates_koguchi_number_WLS_onlypost <- df_estimates #for robustness check

results_koguchi_number_WLS_onlypost <- estimation_results # for only-post DID table

7 Y = emergency small amount funds/緊急小口の決定件数 with covar

7.3 WLS, no trends, post-covid-month dummies, TableC.6 (2)

# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$koguchi_number, 
                    treat_var = df_analysis$unemploy_shock_diff2)

texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)
## 18 coefficients  not defined because the design matrix is rank deficient
## 
## ===================================================================
##                                                         Model 1    
## -------------------------------------------------------------------
## treat_var:date_2020_02                                             
##                                                                    
## treat_var:date_2020_03                                             
##                                                                    
## treat_var:date_2020_04                                     4.930   
##                                                          (26.958)  
## treat_var:date_2020_05                                    60.018   
##                                                          (50.170)  
## treat_var:date_2020_06                                    49.219   
##                                                          (51.683)  
## treat_var:date_2020_07                                    13.598   
##                                                          (27.849)  
## treat_var:date_2020_08                                    20.315   
##                                                          (20.073)  
## treat_var:date_2020_09                                    36.587   
##                                                          (24.173)  
## date_2020_02:google_mobility_index_2020may                         
##                                                                    
## date_2020_03:google_mobility_index_2020may                         
##                                                                    
## date_2020_04:google_mobility_index_2020may                 1.374   
##                                                           (5.892)  
## date_2020_05:google_mobility_index_2020may                -1.316   
##                                                           (5.831)  
## date_2020_06:google_mobility_index_2020may                 0.190   
##                                                           (5.464)  
## date_2020_07:google_mobility_index_2020may                -1.502   
##                                                           (3.367)  
## date_2020_08:google_mobility_index_2020may                -0.620   
##                                                           (2.170)  
## date_2020_09:google_mobility_index_2020may                -0.651   
##                                                           (2.108)  
## date_2020_02:infection_rate_cumulative2020jun                      
##                                                                    
## date_2020_03:infection_rate_cumulative2020jun                      
##                                                                    
## date_2020_04:infection_rate_cumulative2020jun              2.682   
##                                                           (3.092)  
## date_2020_05:infection_rate_cumulative2020jun              5.174   
##                                                           (6.146)  
## date_2020_06:infection_rate_cumulative2020jun              3.776   
##                                                           (6.438)  
## date_2020_07:infection_rate_cumulative2020jun              2.125   
##                                                           (3.591)  
## date_2020_08:infection_rate_cumulative2020jun              1.278   
##                                                           (2.253)  
## date_2020_09:infection_rate_cumulative2020jun              0.788   
##                                                           (2.295)  
## date_2020_02:death_rate_cumulative2020jun                          
##                                                                    
## date_2020_03:death_rate_cumulative2020jun                          
##                                                                    
## date_2020_04:death_rate_cumulative2020jun                 -6.274   
##                                                          (36.560)  
## date_2020_05:death_rate_cumulative2020jun                -29.915   
##                                                          (66.644)  
## date_2020_06:death_rate_cumulative2020jun                -34.880   
##                                                          (73.642)  
## date_2020_07:death_rate_cumulative2020jun                -16.860   
##                                                          (40.827)  
## date_2020_08:death_rate_cumulative2020jun                -12.132   
##                                                          (24.491)  
## date_2020_09:death_rate_cumulative2020jun                -12.020   
##                                                          (24.794)  
## date_2020_02:Population_per_1_km_2_of_inhabitable_area             
##                                                                    
## date_2020_03:Population_per_1_km_2_of_inhabitable_area             
##                                                                    
## date_2020_04:Population_per_1_km_2_of_inhabitable_area    -0.008   
##                                                           (0.011)  
## date_2020_05:Population_per_1_km_2_of_inhabitable_area     0.007   
##                                                           (0.013)  
## date_2020_06:Population_per_1_km_2_of_inhabitable_area     0.010   
##                                                           (0.011)  
## date_2020_07:Population_per_1_km_2_of_inhabitable_area     0.003   
##                                                           (0.007)  
## date_2020_08:Population_per_1_km_2_of_inhabitable_area     0.003   
##                                                           (0.004)  
## date_2020_09:Population_per_1_km_2_of_inhabitable_area     0.002   
##                                                           (0.005)  
## date_2020_02:Secondary_industry_ratio                              
##                                                                    
## date_2020_03:Secondary_industry_ratio                              
##                                                                    
## date_2020_04:Secondary_industry_ratio                   -667.459   
##                                                         (442.774)  
## date_2020_05:Secondary_industry_ratio                   -985.886   
##                                                         (587.080)  
## date_2020_06:Secondary_industry_ratio                   -238.691   
##                                                         (518.826)  
## date_2020_07:Secondary_industry_ratio                   -383.631   
##                                                         (313.221)  
## date_2020_08:Secondary_industry_ratio                   -244.567   
##                                                         (220.753)  
## date_2020_09:Secondary_industry_ratio                   -165.786   
##                                                         (201.059)  
## date_2020_02:Tertiary_industry_ratio                               
##                                                                    
## date_2020_03:Tertiary_industry_ratio                               
##                                                                    
## date_2020_04:Tertiary_industry_ratio                    -455.123   
##                                                         (534.328)  
## date_2020_05:Tertiary_industry_ratio                    -887.245   
##                                                         (735.903)  
## date_2020_06:Tertiary_industry_ratio                    -122.924   
##                                                         (802.274)  
## date_2020_07:Tertiary_industry_ratio                    -428.676   
##                                                         (462.374)  
## date_2020_08:Tertiary_industry_ratio                    -288.427   
##                                                         (290.466)  
## date_2020_09:Tertiary_industry_ratio                    -281.342   
##                                                         (282.172)  
## date_2020_02:Total_population                                      
##                                                                    
## date_2020_03:Total_population                                      
##                                                                    
## date_2020_04:Total_population                             -0.026   
##                                                           (0.072)  
## date_2020_05:Total_population                             -0.266 * 
##                                                           (0.101)  
## date_2020_06:Total_population                             -0.181 * 
##                                                           (0.080)  
## date_2020_07:Total_population                             -0.124 * 
##                                                           (0.052)  
## date_2020_08:Total_population                             -0.101 * 
##                                                           (0.038)  
## date_2020_09:Total_population                             -0.101 **
##                                                           (0.029)  
## date_2020_02:Ratio_of_aged_population                              
##                                                                    
## date_2020_03:Ratio_of_aged_population                              
##                                                                    
## date_2020_04:Ratio_of_aged_population                     -2.168   
##                                                           (2.940)  
## date_2020_05:Ratio_of_aged_population                     -6.500   
##                                                           (3.893)  
## date_2020_06:Ratio_of_aged_population                     -7.335 * 
##                                                           (3.234)  
## date_2020_07:Ratio_of_aged_population                     -5.744 **
##                                                           (2.063)  
## date_2020_08:Ratio_of_aged_population                     -4.188 **
##                                                           (1.532)  
## date_2020_09:Ratio_of_aged_population                     -3.826 **
##                                                           (1.290)  
## -------------------------------------------------------------------
## R^2                                                        0.889   
## Adj. R^2                                                   0.872   
## Num. obs.                                                893       
## RMSE                                                    1091.258   
## N Clusters                                                47       
## ===================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "unemploy_shock_diff2",
                                 estimation_label = "koguchi_number_WLS")

# Event study graph
graph_koguchi_number_WLS_covar_onlypost <- event_study_graph_2nd_safetynet(data = df_estimates ,
                                          graph_title = "koguchi_number_WLS")

ggplotly(graph_koguchi_number_WLS_covar_onlypost)
estimates_koguchi_number_WLS_covar_onlypost <- df_estimates #for robustness check

results_koguchi_number_WLS_covar_onlypost <- estimation_results # for only-post DID table

8 Y= general support funds/総合支援の決定件数

8.3 WLS, no trends, post-covid-month dummies, Table C.5 (4)

# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_notrend(dataset = df_analysis, 
                    outcome_var = df_analysis$sogo_number, 
                    treat_var = df_analysis$unemploy_shock_diff2)

texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)
## 2 coefficients  not defined because the design matrix is rank deficient
## 
## ===================================
##                         Model 1    
## -----------------------------------
## treat_var:date_2020_02             
##                                    
## treat_var:date_2020_03             
##                                    
## treat_var:date_2020_04    -0.410   
##                           (1.522)  
## treat_var:date_2020_05     9.863   
##                          (12.136)  
## treat_var:date_2020_06    54.371   
##                          (29.696)  
## treat_var:date_2020_07    82.429 * 
##                          (31.538)  
## treat_var:date_2020_08    52.048 **
##                          (16.772)  
## treat_var:date_2020_09    43.055   
##                          (23.427)  
## -----------------------------------
## R^2                        0.748   
## Adj. R^2                   0.726   
## Num. obs.                893       
## RMSE                    1070.294   
## N Clusters                47       
## ===================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "unemploy_shock_diff2",
                                 estimation_label = "sogo_number_WLS")

# Event study graph
graph_sogo_number_WLS_onlypost <- event_study_graph_2nd_safetynet(data = df_estimates ,
                                          graph_title = "sogo_number_WLS")

ggplotly(graph_sogo_number_WLS_onlypost)
estimates_sogo_number_WLS_onlypost <- df_estimates #for robustness check

results_sogo_number_WLS_onlypost <- estimation_results # for only-post DID table

9 Y= general support funds/総合支援の決定件数 with covar

9.3 WLS, no trends, post-covid-month dummies, Table C.6 (4)

# DID estimation
estimation_results <- dynamic_onlypost_DID_WLS_notrend_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$sogo_number, 
                    treat_var = df_analysis$unemploy_shock_diff2)

texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)
## 18 coefficients  not defined because the design matrix is rank deficient
## 
## ====================================================================
##                                                         Model 1     
## --------------------------------------------------------------------
## treat_var:date_2020_02                                              
##                                                                     
## treat_var:date_2020_03                                              
##                                                                     
## treat_var:date_2020_04                                     0.008    
##                                                           (3.318)   
## treat_var:date_2020_05                                     9.237    
##                                                          (18.451)   
## treat_var:date_2020_06                                    62.343    
##                                                          (34.059)   
## treat_var:date_2020_07                                    87.782 *  
##                                                          (38.840)   
## treat_var:date_2020_08                                    35.869    
##                                                          (20.207)   
## treat_var:date_2020_09                                    26.995    
##                                                          (20.825)   
## date_2020_02:google_mobility_index_2020may                          
##                                                                     
## date_2020_03:google_mobility_index_2020may                          
##                                                                     
## date_2020_04:google_mobility_index_2020may                 0.350    
##                                                           (0.454)   
## date_2020_05:google_mobility_index_2020may                 0.799    
##                                                           (2.779)   
## date_2020_06:google_mobility_index_2020may                -1.830    
##                                                           (3.922)   
## date_2020_07:google_mobility_index_2020may                -1.842    
##                                                           (3.699)   
## date_2020_08:google_mobility_index_2020may                -1.614    
##                                                           (2.703)   
## date_2020_09:google_mobility_index_2020may                -1.268    
##                                                           (2.713)   
## date_2020_02:infection_rate_cumulative2020jun                       
##                                                                     
## date_2020_03:infection_rate_cumulative2020jun                       
##                                                                     
## date_2020_04:infection_rate_cumulative2020jun              0.440    
##                                                           (0.380)   
## date_2020_05:infection_rate_cumulative2020jun              4.519    
##                                                           (2.535)   
## date_2020_06:infection_rate_cumulative2020jun              6.506    
##                                                           (4.350)   
## date_2020_07:infection_rate_cumulative2020jun              5.190    
##                                                           (4.230)   
## date_2020_08:infection_rate_cumulative2020jun              1.447    
##                                                           (2.425)   
## date_2020_09:infection_rate_cumulative2020jun              3.951    
##                                                           (2.726)   
## date_2020_02:death_rate_cumulative2020jun                           
##                                                                     
## date_2020_03:death_rate_cumulative2020jun                           
##                                                                     
## date_2020_04:death_rate_cumulative2020jun                 -5.812    
##                                                           (4.089)   
## date_2020_05:death_rate_cumulative2020jun                -46.616    
##                                                          (27.526)   
## date_2020_06:death_rate_cumulative2020jun                -65.417    
##                                                          (45.834)   
## date_2020_07:death_rate_cumulative2020jun                -54.131    
##                                                          (45.728)   
## date_2020_08:death_rate_cumulative2020jun                 -9.899    
##                                                          (28.105)   
## date_2020_09:death_rate_cumulative2020jun                -33.765    
##                                                          (30.854)   
## date_2020_02:Population_per_1_km_2_of_inhabitable_area              
##                                                                     
## date_2020_03:Population_per_1_km_2_of_inhabitable_area              
##                                                                     
## date_2020_04:Population_per_1_km_2_of_inhabitable_area     0.000    
##                                                           (0.001)   
## date_2020_05:Population_per_1_km_2_of_inhabitable_area    -0.001    
##                                                           (0.005)   
## date_2020_06:Population_per_1_km_2_of_inhabitable_area     0.002    
##                                                           (0.009)   
## date_2020_07:Population_per_1_km_2_of_inhabitable_area     0.008    
##                                                           (0.008)   
## date_2020_08:Population_per_1_km_2_of_inhabitable_area     0.008    
##                                                           (0.004)   
## date_2020_09:Population_per_1_km_2_of_inhabitable_area     0.007    
##                                                           (0.005)   
## date_2020_02:Secondary_industry_ratio                               
##                                                                     
## date_2020_03:Secondary_industry_ratio                               
##                                                                     
## date_2020_04:Secondary_industry_ratio                    -14.136    
##                                                          (39.020)   
## date_2020_05:Secondary_industry_ratio                    -68.471    
##                                                         (235.838)   
## date_2020_06:Secondary_industry_ratio                   -408.113    
##                                                         (351.577)   
## date_2020_07:Secondary_industry_ratio                   -687.034    
##                                                         (395.623)   
## date_2020_08:Secondary_industry_ratio                   -477.582    
##                                                         (266.863)   
## date_2020_09:Secondary_industry_ratio                   -576.146    
##                                                         (289.630)   
## date_2020_02:Tertiary_industry_ratio                                
##                                                                     
## date_2020_03:Tertiary_industry_ratio                                
##                                                                     
## date_2020_04:Tertiary_industry_ratio                      23.022    
##                                                          (61.421)   
## date_2020_05:Tertiary_industry_ratio                      88.732    
##                                                         (413.203)   
## date_2020_06:Tertiary_industry_ratio                    -470.488    
##                                                         (527.717)   
## date_2020_07:Tertiary_industry_ratio                    -884.672    
##                                                         (547.081)   
## date_2020_08:Tertiary_industry_ratio                    -430.582    
##                                                         (371.719)   
## date_2020_09:Tertiary_industry_ratio                    -620.040    
##                                                         (359.852)   
## date_2020_02:Total_population                                       
##                                                                     
## date_2020_03:Total_population                                       
##                                                                     
## date_2020_04:Total_population                             -0.006    
##                                                           (0.004)   
## date_2020_05:Total_population                             -0.069 ** 
##                                                           (0.026)   
## date_2020_06:Total_population                             -0.176 ***
##                                                           (0.049)   
## date_2020_07:Total_population                             -0.227 ***
##                                                           (0.056)   
## date_2020_08:Total_population                             -0.158 ***
##                                                           (0.041)   
## date_2020_09:Total_population                             -0.177 ***
##                                                           (0.042)   
## date_2020_02:Ratio_of_aged_population                               
##                                                                     
## date_2020_03:Ratio_of_aged_population                               
##                                                                     
## date_2020_04:Ratio_of_aged_population                      0.034    
##                                                           (0.236)   
## date_2020_05:Ratio_of_aged_population                     -1.031    
##                                                           (1.260)   
## date_2020_06:Ratio_of_aged_population                     -3.120    
##                                                           (1.928)   
## date_2020_07:Ratio_of_aged_population                     -5.840 *  
##                                                           (2.427)   
## date_2020_08:Ratio_of_aged_population                     -4.436 *  
##                                                           (1.673)   
## date_2020_09:Ratio_of_aged_population                     -4.909 ** 
##                                                           (1.656)   
## --------------------------------------------------------------------
## R^2                                                        0.874    
## Adj. R^2                                                   0.855    
## Num. obs.                                                893        
## RMSE                                                     780.306    
## N Clusters                                                47        
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "unemploy_shock_diff2",
                                 estimation_label = "sogo_number_WLS")

# Event study graph
graph_sogo_number_WLS_covar_onlypost <- event_study_graph_2nd_safetynet(data = df_estimates ,
                                          graph_title = "sogo_number_WLS")

ggplotly(graph_sogo_number_WLS_covar_onlypost)
estimates_sogo_number_WLS_covar_onlypost <- df_estimates #for robustness check

results_sogo_number_WLS_covar_onlypost <- estimation_results # for only-post DID table

10 Y = housing security benefit/住居確保給付金の決定件数

10.3 WLS, no trends, post-covid-month dummies,Table C.5 (6)

# DID estimation
estimation_results <- dynamic_onlypost_DID_jukyo_WLS(dataset = df_analysis, 
                    outcome_var = df_analysis$jukyo_number, 
                    treat_var = df_analysis$unemploy_shock_diff2)

texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)
## 
## ==================================
##                         Model 1   
## ----------------------------------
## treat_var:date_2020_04    1.028   
##                          (0.711)  
## treat_var:date_2020_05   15.236 * 
##                          (6.932)  
## treat_var:date_2020_06   27.509 **
##                          (9.317)  
## treat_var:date_2020_07   15.259 **
##                          (5.281)  
## treat_var:date_2020_08    6.540 * 
##                          (2.972)  
## treat_var:date_2020_09    4.367 * 
##                          (2.124)  
## ----------------------------------
## R^2                       0.785   
## Adj. R^2                  0.750   
## Num. obs.               423       
## RMSE                    362.045   
## N Clusters               47       
## ==================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "unemploy_shock_diff2",
                                 estimation_label = "jukyo_number_WLS")

# Event study graph
graph_jukyo_number_WLS_onlypost <- event_study_graph(data = df_estimates ,
                                          graph_title = "jukyo_number_WLS")

ggplotly(graph_jukyo_number_WLS_onlypost)
estimates_jukyo_number_WLS_onlypost <- df_estimates #for robustness check

results_jukyo_number_WLS_onlypost <- estimation_results # for only-post DID table

11 Y = housing security benefit/住居確保給付金の決定件数 with covar

11.3 WLS, no trends, post-covid-month dummies, Table C.6 (6)

# DID estimation
estimation_results <- dynamic_onlypost_DID_jukyo_WLS_covar8Xcovid_months(dataset = df_analysis, 
                    outcome_var = df_analysis$jukyo_number, 
                    treat_var = df_analysis$unemploy_shock_diff2)

texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)
## 
## ====================================================================
##                                                         Model 1     
## --------------------------------------------------------------------
## treat_var:date_2020_04                                    -1.147    
##                                                           (0.804)   
## treat_var:date_2020_05                                    -5.345    
##                                                           (3.697)   
## treat_var:date_2020_06                                    -4.412    
##                                                           (2.745)   
## treat_var:date_2020_07                                    -1.220    
##                                                           (2.225)   
## treat_var:date_2020_08                                    -2.033 *  
##                                                           (0.985)   
## treat_var:date_2020_09                                    -1.230    
##                                                           (0.676)   
## date_2020_04:google_mobility_index_2020may                -0.024    
##                                                           (0.148)   
## date_2020_05:google_mobility_index_2020may                -0.060    
##                                                           (0.710)   
## date_2020_06:google_mobility_index_2020may                -1.568 *  
##                                                           (0.750)   
## date_2020_07:google_mobility_index_2020may                -0.943    
##                                                           (0.589)   
## date_2020_08:google_mobility_index_2020may                -0.504 *  
##                                                           (0.240)   
## date_2020_09:google_mobility_index_2020may                -0.319    
##                                                           (0.164)   
## date_2020_04:infection_rate_cumulative2020jun              0.002    
##                                                           (0.072)   
## date_2020_05:infection_rate_cumulative2020jun              0.551    
##                                                           (0.444)   
## date_2020_06:infection_rate_cumulative2020jun              0.415    
##                                                           (0.403)   
## date_2020_07:infection_rate_cumulative2020jun              0.406    
##                                                           (0.312)   
## date_2020_08:infection_rate_cumulative2020jun              0.291 *  
##                                                           (0.141)   
## date_2020_09:infection_rate_cumulative2020jun              0.209    
##                                                           (0.132)   
## date_2020_04:death_rate_cumulative2020jun                  0.478    
##                                                           (0.677)   
## date_2020_05:death_rate_cumulative2020jun                  0.226    
##                                                           (4.425)   
## date_2020_06:death_rate_cumulative2020jun                 -2.383    
##                                                           (4.116)   
## date_2020_07:death_rate_cumulative2020jun                 -3.733    
##                                                           (3.070)   
## date_2020_08:death_rate_cumulative2020jun                 -2.790    
##                                                           (1.541)   
## date_2020_09:death_rate_cumulative2020jun                 -1.978    
##                                                           (1.300)   
## date_2020_04:Population_per_1_km_2_of_inhabitable_area     0.000    
##                                                           (0.000)   
## date_2020_05:Population_per_1_km_2_of_inhabitable_area     0.003 ** 
##                                                           (0.001)   
## date_2020_06:Population_per_1_km_2_of_inhabitable_area     0.005 ***
##                                                           (0.001)   
## date_2020_07:Population_per_1_km_2_of_inhabitable_area     0.002    
##                                                           (0.001)   
## date_2020_08:Population_per_1_km_2_of_inhabitable_area     0.000    
##                                                           (0.000)   
## date_2020_09:Population_per_1_km_2_of_inhabitable_area     0.000    
##                                                           (0.000)   
## date_2020_04:Secondary_industry_ratio                    -23.782 *  
##                                                          (11.672)   
## date_2020_05:Secondary_industry_ratio                   -127.705    
##                                                          (65.678)   
## date_2020_06:Secondary_industry_ratio                   -109.665 ** 
##                                                          (36.263)   
## date_2020_07:Secondary_industry_ratio                    -15.060    
##                                                          (43.642)   
## date_2020_08:Secondary_industry_ratio                    -37.852 ** 
##                                                          (13.102)   
## date_2020_09:Secondary_industry_ratio                    -30.531 ** 
##                                                          (10.044)   
## date_2020_04:Tertiary_industry_ratio                     -12.907    
##                                                          (11.978)   
## date_2020_05:Tertiary_industry_ratio                     -56.344    
##                                                          (75.533)   
## date_2020_06:Tertiary_industry_ratio                     -49.819    
##                                                          (50.714)   
## date_2020_07:Tertiary_industry_ratio                     -14.300    
##                                                          (54.424)   
## date_2020_08:Tertiary_industry_ratio                     -31.769    
##                                                          (19.491)   
## date_2020_09:Tertiary_industry_ratio                     -33.438 *  
##                                                          (13.639)   
## date_2020_04:Total_population                             -0.002    
##                                                           (0.001)   
## date_2020_05:Total_population                             -0.026 *  
##                                                           (0.010)   
## date_2020_06:Total_population                             -0.026 ** 
##                                                           (0.008)   
## date_2020_07:Total_population                             -0.003    
##                                                           (0.007)   
## date_2020_08:Total_population                             -0.003    
##                                                           (0.002)   
## date_2020_09:Total_population                             -0.003    
##                                                           (0.002)   
## date_2020_04:Ratio_of_aged_population                     -0.054    
##                                                           (0.087)   
## date_2020_05:Ratio_of_aged_population                     -0.913 *  
##                                                           (0.414)   
## date_2020_06:Ratio_of_aged_population                     -0.504    
##                                                           (0.360)   
## date_2020_07:Ratio_of_aged_population                     -0.102    
##                                                           (0.271)   
## date_2020_08:Ratio_of_aged_population                     -0.165    
##                                                           (0.118)   
## date_2020_09:Ratio_of_aged_population                     -0.145    
##                                                           (0.091)   
## --------------------------------------------------------------------
## R^2                                                        0.972    
## Adj. R^2                                                   0.963    
## Num. obs.                                                423        
## RMSE                                                     139.595    
## N Clusters                                                47        
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results, 
                                         treat_var = "unemploy_shock_diff2",
                                 estimation_label = "jukyo_number_WLS")

# Event study graph
graph_jukyo_number_WLS_covar_onlypost <- event_study_graph(data = df_estimates ,
                                          graph_title = "jukyo_number_WLS")

ggplotly(graph_jukyo_number_WLS_covar_onlypost)
estimates_jukyo_number_WLS_covar_onlypost <- df_estimates #for robustness check

results_jukyo_number_WLS_covar_onlypost <- estimation_results # for only-post DID table

12 Merge outcome results/アウトカム結果の結合

12.1 emergency small amount funds/緊急小口

#merge and label estimates data
estimates_koguchi_number_bind <- dplyr::bind_rows(estimates_koguchi_number_OLS, 
                                               estimates_koguchi_number_WLS)

#change labels and reorder labels
estimates_koguchi_number_bind <- estimates_labeling_poverty(estimates_koguchi_number_bind)

#graph
graph_koguchi_number_bind <- event_study_graph_bind_legend(data = estimates_koguchi_number_bind, 
                                             graph_title = "Emergency Small Amount Funds") +
  theme(legend.position = 'none')

ggplotly(graph_koguchi_number_bind)

12.2 emergency small amount funds/緊急小口 with covar

#merge and label estimates data
estimates_koguchi_number_bind <- dplyr::bind_rows(estimates_koguchi_number_OLS_covar, 
                                           estimates_koguchi_number_WLS_covar)

#change labels and reorder labels
estimates_koguchi_number_bind <- estimates_labeling_poverty(estimates_koguchi_number_bind)

#graph
graph_koguchi_number_bind_covar <- event_study_graph_bind_legend(data = estimates_koguchi_number_bind, 
                                             graph_title = "Emergency Small Amount Funds, with covariates") +
  theme(legend.position = 'none')

ggplotly(graph_koguchi_number_bind_covar)

12.3 general support funds/総合支援

#merge and label estimates data
estimates_sogo_number_bind <- dplyr::bind_rows(estimates_sogo_number_OLS, 
                                               estimates_sogo_number_WLS)

#change labels and reorder labels
estimates_sogo_number_bind <- estimates_labeling_poverty(estimates_sogo_number_bind)

#graph
graph_sogo_number_bind <- event_study_graph_bind_legend(data = estimates_sogo_number_bind, 
                                             graph_title = "General Support Funds") + 
  theme(legend.position = 'none')

ggplotly(graph_sogo_number_bind)

12.4 general support funds/総合支援 with covar

#merge and label estimates data
estimates_sogo_number_bind <- dplyr::bind_rows(estimates_sogo_number_OLS_covar, 
                                               estimates_sogo_number_WLS_covar)

#change labels and reorder labels
estimates_sogo_number_bind <- estimates_labeling_poverty(estimates_sogo_number_bind)

#graph
graph_sogo_number_bind_covar <- event_study_graph_bind_legend(data = estimates_sogo_number_bind, 
                                             graph_title = "General Support Funds, with covariates") +
  theme(legend.position = 'none')

ggplotly(graph_sogo_number_bind_covar)

12.5 housing security benefit/居住確保給付金

#merge and label estimates data
estimates_jukyo_number_bind <- dplyr::bind_rows(estimates_jukyo_number_OLS, 
                                                estimates_jukyo_number_WLS)

#change labels and reorder labels
estimates_jukyo_number_bind <- estimates_labeling_poverty(estimates_jukyo_number_bind)

#graph
graph_jukyo_number_bind <- event_study_graph_bind_legend(data = estimates_jukyo_number_bind, 
                                             graph_title = "Housing Security Benefit") +
  theme(legend.position = 'none')

ggplotly(graph_jukyo_number_bind)

12.6 housing security benefit/居住確保給付金 with covar

#merge and label estimates data
estimates_jukyo_number_bind <- dplyr::bind_rows(estimates_jukyo_number_OLS_covar, 
                                                estimates_jukyo_number_WLS_covar)

#change labels and reorder labels
estimates_jukyo_number_bind <- estimates_labeling_poverty(estimates_jukyo_number_bind)

#graph
graph_jukyo_number_bind_covar <- event_study_graph_bind_legend(data = estimates_jukyo_number_bind, 
                                             graph_title = "Housing Security Benefit, with covariates") +
  theme(legend.position = 'none')

ggplotly(graph_jukyo_number_bind_covar)

12.7 GGplotly

ggplotly(graph_koguchi_number_bind)
ggplotly(graph_koguchi_number_bind_covar)
ggplotly(graph_sogo_number_bind)
ggplotly(graph_sogo_number_bind_covar)
ggplotly(graph_jukyo_number_bind)
ggplotly(graph_jukyo_number_bind_covar)

13 Merge graphs/グラフ統合

13.1 Extract legend/legend取り出し

#Legendの表示

graph_for_legend  <- graph_koguchi_number_bind +
 theme(legend.position = 'bottom', # Adjust x axis label
       legend.title = element_text(color = "black", size = 20),
       legend.text = element_text(color = "black", size = 20))
graph_for_legend  
## Warning: Removed 4 rows containing missing values (geom_point).

#extract legend
legend_model_types <- ggpubr::get_legend(graph_for_legend)
## Warning: Removed 4 rows containing missing values (geom_point).
legend_model_types <- ggpubr::as_ggplot(legend_model_types)
legend_model_types

13.2 Merge/統合

グラフを統合して論文用に保存。 ### graph size

dpi_num <- 100
width_num <- 15
height_num <- 15

13.2.1 WLS

ymin <- - 50
ymax <- 155

ymin_num <- - 50
ymax_num  <- 150
interval <- 25

graph_koguchi_number_WLS <- graph_koguchi_number_WLS + 
  labs(title = "(a) Emergency Small Amount Funds") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_koguchi_number_WLS_covar <- graph_koguchi_number_WLS_covar + 
  labs(title = "(b) Emergency Small Amount Funds, with covariates") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_sogo_number_WLS <- graph_sogo_number_WLS + 
  labs(title =  "(c) General Support Funds")+ 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_sogo_number_WLS_covar <- graph_sogo_number_WLS_covar + 
  labs(title = "(d) General Support Funds, with covariates") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_jukyo_number_WLS <- graph_jukyo_number_WLS + 
  labs(title = "(e) Housing Security Benefit") + 
  scale_y_continuous(limit = c(-15, 50), breaks=seq(-10, 50, 10))

graph_jukyo_number_WLS_covar <- graph_jukyo_number_WLS_covar + 
  labs(title = "(f) Housing Security Benefit, with covariates") + 
  scale_y_continuous(limit = c(-15, 50), breaks=seq(-10, 50, 10))

graph <- (graph_koguchi_number_WLS | graph_koguchi_number_WLS_covar) / 
  (graph_sogo_number_WLS | graph_sogo_number_WLS_covar) /
    (graph_jukyo_number_WLS | graph_jukyo_number_WLS_covar) 

graph
## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 12 rows containing missing values (geom_point).

## Warning: Removed 12 rows containing missing values (geom_point).

#保存
ggsave(file = "output/graph_unemploy_diff2_on_2nd_safetynet_WLS.pdf", plot = graph, 
       dpi = dpi_num, width = width_num, height = height_num)  
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 12 rows containing missing values (geom_point).

## Warning: Removed 12 rows containing missing values (geom_point).

13.2.2 Robustness check

# 2021Feb12Waki

ymin <- - 55
ymax <- 175

ymin_num <- - 50
ymax_num  <- 175
interval <- 25

graph_koguchi_number_bind  <- graph_koguchi_number_bind + 
  labs(title = "(a) Emergency Small Amount Funds") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_koguchi_number_bind_covar  <- graph_koguchi_number_bind_covar+ 
  labs(title = "(b) Emergency Small Amount Funds, with covariates") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_sogo_number_bind  <- graph_sogo_number_bind+ 
  labs(title =  "(c) General Support Funds") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_sogo_number_bind_covar  <- graph_sogo_number_bind_covar+ 
  labs(title = "(d) General Support Funds, with covariates") + 
  scale_y_continuous(limit = c(ymin, ymax), breaks=seq(ymin_num, ymax_num, interval))

graph_jukyo_number_bind  <- graph_jukyo_number_bind+ 
  labs(title = "(e) Housing Security Benefit") + 
  scale_y_continuous(limit = c(-15, 50), breaks=seq(-10, 50, 10))

graph_jukyo_number_bind_covar  <-  graph_jukyo_number_bind_covar+ 
  labs(title = "(f) Housing Security Benefit, with covariates") + 
  scale_y_continuous(limit = c(-15, 50), breaks=seq(-10, 50, 10))

graph_2nd_tier_diff_models <-  (graph_koguchi_number_bind + graph_koguchi_number_bind_covar)/
                                (graph_sogo_number_bind + graph_sogo_number_bind_covar)/
                                (graph_jukyo_number_bind + graph_jukyo_number_bind_covar)/legend_model_types + 
plot_layout(heights = c(2,2,2, 0.5))  #0.3から0.5へ変更 2021Sep7 Waki
 
 
graph_2nd_tier_diff_models
## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing missing values (geom_point).

#保存
ggsave(file = "output/graph_unemploy_diff2_on_2nd_safetynet_robust.pdf", plot = graph_2nd_tier_diff_models, 
       dpi = dpi_num, width = width_num, height = height_num)     
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 24 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing missing values (geom_point).

14 Regression table/回帰結果表 without covar

options("modelsummary_format_numeric_latex" = "plain")

# 列の選択 column order

# 緊急小口、総合支援、住居確保、YOYのみ, monthlyhのみ

rows_MONTH <- tribble(~name, ~"(1)", ~"(2)", ~"(3)", ~"(4)", ~"(5)", ~"(6)", 
"Ref. month", "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}",  "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}","\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}")

## results list
table_results_MONTH <- list()
table_results_MONTH[["(1)"]] <- results_koguchi_number_WLS
table_results_MONTH[["(2)"]] <- results_koguchi_number_WLS_onlypost
table_results_MONTH[["(3)"]] <- results_sogo_number_WLS
table_results_MONTH[["(4)"]] <- results_sogo_number_WLS_onlypost
table_results_MONTH[["(5)"]] <- results_jukyo_number_WLS
table_results_MONTH[["(6)"]] <- results_jukyo_number_WLS_onlypost

## HTML table
estimates_table_MONTH(df = table_results_MONTH,
                      rows = rows_MONTH,
                      title_words = "UIbenefit",
                      gof = gm,
                      output_style = "html") %>%
    kableExtra::add_header_above(c(" " = 1, "Emergency S.A." = 2, "General Support" = 2, "Housing Security" = 2))
## 14 coefficients  not defined because the design matrix is rank deficient
## 2 coefficients  not defined because the design matrix is rank deficient
## 14 coefficients  not defined because the design matrix is rank deficient
## 2 coefficients  not defined because the design matrix is rank deficient
UIbenefit
Emergency S.A.
General Support
Housing Security
Apr. 2020 18.723 18.693 −0.395 −0.410 1.103 1.028
(21.950) (21.755) (1.539) (1.522) (0.730) (0.711)
May. 2020 73.840 73.809 9.879 9.863 15.311 15.236
(42.803) (42.459) (12.245) (12.136) (6.970) (6.932)
Jun. 2020 88.768 88.737 54.387 54.371 27.584 27.509
(32.722) (32.461) (29.934) (29.696) (9.358) (9.317)
Jul. 2020 34.689 34.658 82.444 82.429 15.334 15.259
(21.224) (21.043) (31.787) (31.538) (5.316) (5.281)
Aug. 2020 27.162 27.131 52.064 52.048 6.615 6.540
(14.477) (14.347) (16.912) (16.772) (2.999) (2.972)
Sep. 2020 21.875 21.844 43.071 43.055 4.442 4.367
(15.342) (15.209) (23.620) (23.427) (2.152) (2.124)
Sample size 893 893 893 893 423 423
R2 Adj. 0.795 0.798 0.722 0.726 0.749 0.750
Ref. month {Jan.2020} {\(\leq\)Jan.2020} {Jan.2020} {\(\leq\)Jan.2020} {Jan.2020} {\(\leq\)Jan.2020}
## Latex table
estimates_table_MONTH(df = table_results_MONTH,
                      rows = rows_MONTH,
                      gof = gm,
                      title_words = "Estimation results for second-tier safety net, without covariates", 
                      output_style = "latex") %>% 
  kableExtra::add_header_above(c(" " = 1, "Emergency S.A." = 2, "General Support" = 2, "Housing Security" = 2)) %>%
  kableExtra::add_footnote(c("Notes: Columns (1), (3), and (5) present baseline WLS estimates shown in  the left-hand side of Figure \\ref{fig:DID_unemploy_on_2nd_safetynet}. Columns (2), (4), and (6) present WLS estimates based on the model \\eqref{eq:did_model_ver2}, weighted by prefecture population size, but individual linear trends are not incorporated. The treatment variable is the COVID-19-induced employment shock, which is calculated as equation \\eqref{eq:employment_shock}. Robust standard errors are clustered at the prefecture level."),threeparttable = TRUE, notation = "none",escape = FALSE) %>% 
  kableExtra::column_spec(2:5, width = "1.5cm") %>% 
  kableExtra::save_kable("output/table_unemploy_diff2_on_2nd_safetynet_robust.tex")
## 14 coefficients  not defined because the design matrix is rank deficient
## 
## 2 coefficients  not defined because the design matrix is rank deficient
## 14 coefficients  not defined because the design matrix is rank deficient
## 2 coefficients  not defined because the design matrix is rank deficient

15 Regression table/回帰結果表 with covar

# 列の選択 column order

# 緊急小口、総合支援、住居確保、YOYのみ, monthlyhのみ

rows_MONTH <- tribble(~name, ~"(1)", ~"(2)", ~"(3)", ~"(4)", ~"(5)", ~"(6)", 
"Ref. month", "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}",  "\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}","\\footnotesize{Jan.2020}", "\\footnotesize{$\\leq$Jan.2020}")

## results list
table_results_MONTH <- list()
table_results_MONTH[["(1)"]] <- results_koguchi_number_WLS_covar
table_results_MONTH[["(2)"]] <- results_koguchi_number_WLS_covar_onlypost
table_results_MONTH[["(3)"]] <- results_sogo_number_WLS_covar
table_results_MONTH[["(4)"]] <- results_sogo_number_WLS_covar_onlypost
table_results_MONTH[["(5)"]] <- results_jukyo_number_WLS_covar
table_results_MONTH[["(6)"]] <- results_jukyo_number_WLS_covar_onlypost

## HTML table
estimates_table_MONTH(df = table_results_MONTH,
                      rows = rows_MONTH,
                      title_words = "UIbenefit",
                      gof = gm,
                      output_style = "html") %>%
    kableExtra::add_header_above(c(" " = 1, "Emergency S.A." = 2, "General Support" = 2, "Housing Security" = 2))
## 30 coefficients  not defined because the design matrix is rank deficient
## 18 coefficients  not defined because the design matrix is rank deficient
## 30 coefficients  not defined because the design matrix is rank deficient
## 18 coefficients  not defined because the design matrix is rank deficient
UIbenefit
Emergency S.A.
General Support
Housing Security
Apr. 2020 4.941 4.930 0.002 0.008 −1.072 −1.147
(27.175) (26.958) (3.346) (3.318) (0.807) (0.804)
May. 2020 60.029 60.018 9.231 9.237 −5.270 −5.345
(50.569) (50.170) (18.599) (18.451) (3.709) (3.697)
Jun. 2020 49.230 49.219 62.337 62.343 −4.337 −4.412
(52.096) (51.683) (34.330) (34.059) (2.750) (2.745)
Jul. 2020 13.609 13.598 87.776 87.782 −1.145 −1.220
(28.076) (27.849) (39.148) (38.840) (2.236) (2.225)
Aug. 2020 20.326 20.315 35.863 35.869 −1.958 −2.033
(20.236) (20.073) (20.370) (20.207) (0.986) (0.985)
Sep. 2020 36.598 36.587 26.989 26.995 −1.155 −1.230
(24.363) (24.173) (20.992) (20.825) (0.682) (0.676)
Sample size 893 893 893 893 423 423
R2 Adj. 0.870 0.872 0.852 0.855 0.963 0.963
Ref. month {Jan.2020} {\(\leq\)Jan.2020} {Jan.2020} {\(\leq\)Jan.2020} {Jan.2020} {\(\leq\)Jan.2020}
## Latex table
estimates_table_MONTH(df = table_results_MONTH,
                      rows = rows_MONTH,
                      gof = gm,
                      title_words = "Estimation results for second-tier safety net, with covariates", 
                      output_style = "latex") %>% 
  kableExtra::add_header_above(c(" " = 1, "Emergency S.A." = 2, "General Support" = 2, "Housing Security" = 2)) %>%
  kableExtra::add_footnote(c("Notes:  Columns (1), (3), and (5) present WLS estimates shown in the right-hand side of Figure \\ref{fig:DID_unemploy_on_2nd_safetynet}. Columns (2), (4), and (6) present WLS estimates based on the model \\eqref{eq:did_model_ver2}, weighted by prefecture population size, but individual linear trends are not incorporated and eight covariates are additionally controlled for. The treatment variable is the COVID-19-induced employment shock, which is calculated as equation \\eqref{eq:employment_shock}. Robust standard errors are clustered at the prefecture level."),threeparttable = TRUE, notation = "none",escape = FALSE) %>% 
  kableExtra::column_spec(2:5, width = "1.5cm") %>% 
  kableExtra::save_kable("output/table_unemploy_diff2_on_2nd_safetynet_robust_covar.tex")
## 30 coefficients  not defined because the design matrix is rank deficient
## 
## 18 coefficients  not defined because the design matrix is rank deficient
## 30 coefficients  not defined because the design matrix is rank deficient
## 18 coefficients  not defined because the design matrix is rank deficient